Gas hydrate solids occurrence is considered as one of the serious challenges in flow assurance as it affects the hydrocarbon production significantly, especially in deep water gas fields. The most cost-effective method to inhibit the formation of hydrate in pipelines is by injecting a hydrate inhibitor agent. Continuous studies have led to a comprehensive understanding on the use of low dosage hydrate inhibitors such as ionic liquid and quaternary ammonium salts which are also known as dual function gas hydrate inhibitors. This paper covers the latest types of quaternary ammonium salts (2020–2016) and a summary of findings which are essential for future studies. Reviews on the effects of length of ionic liquids alkyl chain, average suppression temperatures, hydrate dissociation enthalpies, and electrical conductivity to the effectiveness of the quaternary ammonium salts as gas hydrate inhibitors are included.

The current oil industry is moving towards digitalization, which is a good opportunity that will bring value to all its stakeholders. The digitalization of oil and gas discovery, which are production-based industries, is driven by enabling technologies which include machine learning (ML) and big data analytics. However, the existing Metocean system generates data manually using sensors such as the wave buoy, anemometer, and acoustic doppler current profiler (ADCP). Additionally, these data which appear in ASCII format to the Metocean system are also manual and silos. This slows down provisioning, while the monitoring element of the Metocean data path is partial. In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the in-depth integration of Metocean data interoperability for intelligent operations and automation using an ML-based approach, is presented. A new model integrated with the existing Metocean data system using ML algorithms to monitor and interoperate with maximum performance is proposed. The study reveals that ML is one of the crucial and key enabling tools that the oil and gas industries are now focused on for implementing digital transformation, which allows the industry to automate, enhance production, and have less human capacity. Lastly, user recommendations for potential future investigations are offered.

Transportation of waxy crude oil in a production pipeline often encountered flow assurance issues, such as wax deposition. In a case where pipeline shutdown is needed, wax deposit is likely to form within the pipelines, which leads to operational complexity during the restart phase. Commonly, crude oil restarts to flow after a significantly high restart pressure is pumped longer than is necessary. This is due to the physical hindrance caused by the solid wax, which requires additional pressure to disintegrate it before achieving a steady crude oil flow. This study aims to investigate the effect of crude oil and nitrogen gas flow rates on the time taken for crude oil flow initiation using a flow loop rig, which is connected to a nitrogen gas injection system. The nitrogen gas was injected into the test section pipeline at predetermined flow rates within specified periods. After 45 min of static cooling, the crude oil gear pump is switched on to build sufficient pressure to initiate the waxy crude oil flow in the pipeline. Additionally, a statistical analysis by the response surface methodology was also performed by Minitab^® 19 software. Results show that the maximum reduction in flow initiation is 73.7% at 5 L/min of crude oil and 1 L/min of nitrogen gas. This study reveals that the presence of nitrogen gas improved the pipeline restart phase by minimizing both restart pressure and time taken for flow initiation.

Crude oil emulsion is causing a lot of problems, especially during crude oil production. There are many ways to mitigate the emulsion problems but this leads to an increment in operating expenses of oil production. In order to comply with the standard sales oil quality, crude oil emulsion must be treated properly. Hence, better understanding of emulsion is essential since emulsion can be available in almost all phases of oil production and processing. This chapter describes how temperature parameters would affect the rheological property of a low-viscous emulsion and how it would become a significant point associated with stability of crude oil emulsion in oilfield production. Experimental results indicated that the water-in-crude oil emulsion formed from low-viscous crude oil exhibits a non-Newtonian shear thinning behavior, which was best presented by the Herschel-Bulkley rheological model. Temperature ranges from 20 to 90°C were examined to study the effect of temperature toward shear stress and viscosity of oilfield emulsion. Measurement of shear stress at shear rates higher than 600 s−1 is a new direction in rheology study that not much is known about its effect on shear stress.